Lung cancer affects both men and women. At least one set of statistics has claimed that 90,363 men and 65,606 women died from lung cancer in the United States in 2001. It is believed that the early detection of lung cancer can increase the five-year survival rate from 12% to 70%. Screening for lung cancer can help with early detection.
Projection radiographs of the chest can be used for screening for lung cancer. In a chest radiograph, lung cancer appears as opaque, lumpy, nodules within the lung. When nodules in chest radiographs are detected, further steps can taken to diagnose the pulmonary nodule as benign or malignant and treat the patient accordingly. However, for a variety of reasons including viewer fatigue and nodule occlusion by ribs, nodules can go undetected in chest radiographs. Some statistics indicate that physicians miss approximately 30% of nodules in chest radiographs. In such cases, the cancer could go untreated and the patient's chance of surviving the cancer could be reduced.
Computer assisted detection or computed aided detection (CAD) has been employed to decrease a false negatives in lung cancer detection. CAD can help physicians find pulmonary nodules and consequently increase a patient's chance of surviving lung cancer.
U.S. Pat. No. 5,987,094 (Clarke) is directed to a computer-assisted method and apparatus for the detection of lung nodules.
U.S. Pat. No. 6,240,201 (Xu) is directed to a method for nodule detection in chest radiographs. The method employs a soft tissue image in addition to a standard radiograph, which is not always available.
In the CAD method disclosed in U.S. Pat. No. 6,141,437 (Xu), several thresholds are applied to a radiograph to identify candidate nodule regions. However, it has been viewed that applying one or more thresholds to an image may not be sufficient means of nodule segmentation.
In the method disclosed in U.S. Pat. No. 6,549,646 (Yeh), a clear lung field is divided into multiple zones and individually optimizes nodule detection for each zone. This techniques involves discarding pixels outside the clear lung field which may reduce the ability to detect nodules near the clear lung field boundary.
In U.S. Pat. No. 6,683,973 (Li), an area in a chest radiograph is compared to templates that are characteristic of both normal and abnormal anatomy. The effectiveness of this approach has been considered to be limited because the characteristics of abnormal anatomy can not be anticipated when the templates are created.
Other disclosures related to pulmonary nodule detection in chest radiographs are known, including: U.S. Pat. No. 6,088,473 (Xu), U.S. Pat. No. 6,760,468 (Yeh), U.S. Pat. No. 6,058,322 (Nishikawa), U.S. Pat. No. 6,654,728 (Li), U.S. Pat. No. 6,078,680 (Yoshida), U.S. Pat. No. 6,754,380 (Suzuki), U.S. Pat. No. 5,289,374 (Doi), and U.S. Pat. No. 6,125,194 (Yeh).
Although automatic detection of pulmonary nodules in a chest radiograph has been a topic of research for several years, it remains a challenging problem for several reasons. In a projection chest radiograph normal anatomy and pulmonary nodules are superimposed making them difficult to distinguish. Also, normal anatomy such as rib crossings and pulmonary blood vessels can have the appearance of a pulmonary nodule. In addition, pulmonary nodules vary widely in size, shape, density, and other characteristics.
Accordingly, there still exists a need for automatic detection of pulmonary nodules in a chest radiograph which is robust and overcomes at least one of the disadvantages/problems of existing systems/methods.